DECENT WORK AND ECONOMIC GROWTH Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all
Indicator 8.3.1: Proportion of informal employment in total employment, by sector and sex
Indicator 8.4.1: Material footprint, material footprint per capita, and material footprint per GDP
Indicator 8.4.2: Domestic material consumption, domestic material consumption per capita, and domestic material consumption per GDP
Indicator 8.5.1: Average hourly earnings of employees, by sex, age, occupation and persons with disabilities
Indicator 8.6.1: Proportion of youth (aged 15–24 years) not in education, employment or training
Indicator 8.7.1: Proportion and number of children aged 5–17 years engaged in child labour, by sex and age
Indicator 8.8.1: Fatal and non-fatal occupational injuries per 100,000 workers, by sex and migrant status
Indicator 8.8.2: Level of national compliance with labour rights (freedom of association and collective bargaining) based on International Labour Organization (ILO) textual sources and national legislation, by sex and migrant status
Indicator 8.9.1: Tourism direct GDP as a proportion of total GDP and in growth rate
Indicator 8.10.1: (a) Number of commercial bank branches per 100,000 adults and (b) number of automated teller machines (ATMs) per 100,000 adults
Indicator 8.10.2: Proportion of adults (15 years and older) with an account at a bank or other financial institution or with a mobile-money-service provider
Indicator 8.b.1: Existence of a developed and operationalized national strategy for youth employment, as a distinct strategy or as part of a national employment strategy
Cross Cutting Story
Examine impacts of economic growth on inequality. Look at spells of growth and impacts on inequality.
Indicator 10.1.1: Growth rates of household expenditure or income per capita among the bottom 40 per cent of the population and the total population
Indicator 10.2.1: Proportion of people living below 50 per cent of median income, by sex, age and persons with disabilities
#pull gdp and inequality data from the WDI
cc_indicators <- c(
#'NY.GDP.MKTP.KD', #GDP (constant 2015 US$
#'NY.GDP.MKTP.KD.ZG', #GDP growth (annual %)
'NY.GDP.PCAP.KD', #GDP per capita (constant 2015 US$)
'NY.GDP.PCAP.KD.ZG', #GDP per capita growth (annual %)
# 'NY.GNP.MKTP.KD', #GNI (constant 2015 US$)
#'NY.GNP.MKTP.KD.ZG', #GNI growth (annual %)
#'NY.GNP.PCAP.KD', #GNI per capita (constant 2015 US$)
#'NY.GNP.PCAP.KD.ZG', #GNI per capita growth (annual %)
'NY.GNP.PCAP.PP.KD', #GNI per capita, PPP (constant 2017 international $)
'SI.POV.GINI', #Gini index (World Bank estimate)
'SI.SPR.PC40', #Survey mean consumption or income per capita, bottom 40% of population (2011 PPP $ per day)
'SI.SPR.PC40.ZG' #Annualized average growth rate in per capita real survey mean consumption or income, bottom 40% of population (%)
)
cross_cut_df <- wb_data(
indicator = cc_indicators,
start_date=1995,
end_date=2019,
mrv=10,
gapfill = TRUE
)
cross_cut_df <- cross_cut_df %>%
left_join(wb_countries())
Compare Gini versus Log GDP
library(WDI)
#create a new data frame containing 5 year average of gdp and gini to eliminate volatility
gini_gdp_df <- WDI(
indicator = c('NY.GNP.PCAP.PP.KD','SI.POV.GINI','SP.POP.TOTL'),
start=1980,
end=2019,
extra=TRUE
) %>%
dplyr::arrange(iso3c,-year) %>%
filter(region!="Aggregates") %>%
group_by(country, iso3c) %>%
fill(NY.GNP.PCAP.PP.KD, SI.POV.GINI, .direction="downup") %>%
mutate(
date=year,
gni_pcap_3yr=rollmean(NY.GNP.PCAP.PP.KD, 3, fill=NA, align='left'),
gni_pcap_5yr=rollmean(NY.GNP.PCAP.PP.KD, 5, fill=NA, align='left'),
gini_3yr=rollmean(SI.POV.GINI, 3, fill=NA, align='left'),
gini_5yr=rollmean(SI.POV.GINI, 5, fill=NA, align='left')
)
gini_gdp_df_1980 <- gini_gdp_df %>%
filter(date==1980)
gini_gdp_df_2019 <- gini_gdp_df %>%
filter(date==2019)
#create dataframe of changes from 2019 to 1980
gni_pcap_change <- gini_gdp_df_2019$NY.GNP.PCAP.PP.KD - gini_gdp_df_1980$NY.GNP.PCAP.PP.KD
gini_change <- gini_gdp_df_2019$SI.POV.GINI - gini_gdp_df_1980$SI.POV.GINI
iso3c <- gini_gdp_df_2019$iso3c
country <- gini_gdp_df_2019$country
gini_gdp_df_chg <- data.frame(
country=country,
iso3c=iso3c,
region=gini_gdp_df_2019$region,
gni_pcap_change=gni_pcap_change,
gini_change=gini_change,
population_2019 = gini_gdp_df_2019$SP.POP.TOTL
)
ggplot(gini_gdp_df_2019, aes(x=gni_pcap_change, y=gini_change, color=region, size=SP.POP.TOTL)) +
geom_hline(yintercept=0) +
geom_vline(xintercept=0) +
geom_text(aes(label=iso3c)) +
geom_smooth() +
scale_x_continuous(labels=scales::comma) +
theme_bw() +
theme(legend.position = 'bottom') +
ggtitle("Change between GNI per capita and Gini coefficient for countries from 1980 to 2019.")
ggplot(gini_gdp_df_2019, aes(x=gni_pcap_3yr, y=gini_3yr, color=region, size=SP.POP.TOTL)) +
geom_text(aes(label=iso3c)) +
geom_smooth() +
scale_x_log10(labels=scales::comma) +
theme_bw() +
ggtitle("Relationship between Log GNI per capita and Gini coefficient for countries based on 3 year moving average.")
ggplot(gini_gdp_df_2019, aes(x=gni_pcap_5yr, y=gini_5yr, color=region, size=SP.POP.TOTL)) +
geom_text(aes(label=iso3c)) +
geom_smooth() +
scale_x_log10(labels=scales::comma) +
theme_bw() +
ggtitle("Relationship between Log GNI per capita and Gini coefficient for countries based on 5 year moving average.")
ggplot(gini_gdp_df, aes(x=gni_pcap_5yr, y=gini_5yr, group=iso3c, color=region, size=SP.POP.TOTL)) +
geom_line(size=.5, alpha=0.2) +
geom_text(data=gini_gdp_df_2019, aes(label=iso3c)) +
#geom_point(data=gini_gdp_df_2019) +
scale_x_log10(labels=scales::comma) +
theme_bw() +
ggtitle("Relationship between Log GNI per capita and Gini coefficient for countries based on 5 year moving average.")
#plotly plot over time
fig <- plot_ly(data=gini_gdp_df,
x= ~gni_pcap_5yr, y= ~gini_5yr,frame=~date,group=~country,text=~country,size=~SP.POP.TOTL,color=~region,
type='scatter',
mode='markers',
sizes = c(10, 80)
) %>%
layout(xaxis=list(type="log",
tickvals=c(1000,3000,6000,10000,30000,45000),
tickformat='$,.0f')) %>%
animation_opts(
frame = 50,
transition = 1,
redraw = FALSE
)
fig
Compare annualized GDP growth with annualized growth in per capita mean consumption or income for bottom 40%
ggplot(
cross_cut_df,
aes(x=NY.GDP.PCAP.KD.ZG, y=SI.SPR.PC40.ZG)
) +
geom_point() +
geom_smooth()
elasticity_gdp_bottom40 <- lm(SI.SPR.PC40.ZG ~ NY.GDP.PCAP.KD.ZG, data=cross_cut_df)
sandwich(elasticity_gdp_bottom40)
## (Intercept) NY.GDP.PCAP.KD.ZG
## (Intercept) 0.034943558 -0.001532432
## NY.GDP.PCAP.KD.ZG -0.001532432 0.002296924
vcovHC(elasticity_gdp_bottom40, type = "HC3")
## (Intercept) NY.GDP.PCAP.KD.ZG
## (Intercept) 0.03557087 -0.001676810
## NY.GDP.PCAP.KD.ZG -0.00167681 0.002396948
summary(elasticity_gdp_bottom40)
##
## Call:
## lm(formula = SI.SPR.PC40.ZG ~ NY.GDP.PCAP.KD.ZG, data = cross_cut_df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.0833 -2.2555 -0.2387 2.0870 11.7110
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.6684 0.1879 14.203 < 2e-16 ***
## NY.GDP.PCAP.KD.ZG 0.1711 0.0376 4.549 7.9e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.238 on 295 degrees of freedom
## (1873 observations deleted due to missingness)
## Multiple R-squared: 0.06554, Adjusted R-squared: 0.06237
## F-statistic: 20.69 on 1 and 295 DF, p-value: 7.895e-06
feols(SI.SPR.PC40.ZG ~ NY.GDP.PCAP.KD.ZG, data=cross_cut_df, se="hetero")
## OLS estimation, Dep. Var.: SI.SPR.PC40.ZG
## Observations: 297
## Standard-errors: Heteroskedasticity-robust
## Estimate Std. Error t value Pr(>|t|))
## (Intercept) 2.668400 0.187565 14.2270 < 2.2e-16 ***
## NY.GDP.PCAP.KD.ZG 0.171046 0.048088 3.5569 0.000437 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## RMSE: 3.2268 Adj. R2: 0.062372